Customer service
Answers to recurring questions, initial qualification of inquiries and routing each case to the right person. The model answers from your materials: procedures, price lists and documents.
A model wired into a concrete task: it reads your documents, answers your customers' questions, prepares content and passes the result to the system you work in. With the source cited for every answer, a record of every call, and the bill calculated before launch.
Use cases
Below are the tasks where models perform best today: working with text, documents and recurring questions. What they share is that someone at your company reads or writes the same thing over and over.
Answers to recurring questions, initial qualification of inquiries and routing each case to the right person. The model answers from your materials: procedures, price lists and documents.
Reading an invoice, a contract or an order and pulling the data into your system. Summarizing a long letter and pointing out the places that need a decision.
Product descriptions, offers, blog posts and social posts prepared in your voice, from the materials you already have.
A question asked in plain language, an answer from your procedures, price lists and documentation, along with a pointer to the file it came from.
Summaries written out in words, anomaly detection and a short recap of what changed this week.
The model wired into an automation: it reads a request, works out what it concerns and passes it on with a ready summary.
Implementation levels
We almost always start at the first level, because it shows fastest whether the quality is good enough. The next ones come once the previous level has proven itself.
Configuring existing tools and connecting them to your accounts and data. The fastest way in and the lowest cost.
The model answers from your documents, procedures and price lists, citing the source of every answer. Embedded on your site, in a panel or in a messenger.
AI as part of a larger whole: it reads, grades, generates and passes the result on to your system. With a queue, limits and handling for the cases where the model gets it wrong.
Examples from my products
Each of the following runs in my own products and serves real users. These are the same mechanisms I carry over into client implementations. One of them, a shared page-reading service used by three products, has its own write-up on the blog: one scraper, three AI apps.
In Matury Online the model grades essays against the official exam board's criteria and justifies every score. The student sees exactly where the points were lost.
The same approach can grade applications, offers or submissions against your own list of criteria.
Cytado builds citations from real documents, reads the printed page numbers and quotes them with every citation. When the source is missing, it says so plainly.
The same mechanism keeps an assistant at your company answering strictly from the documents.
Smart-Copy takes a text through consecutive steps: finding sources, an outline for approval, section-by-section writing, artwork at the end. Each stage can be checked on its own.
I break complex tasks into stages, so you can see exactly where something went off course.
Jobs in Smart-Copy go through a queue, the cost of each is tracked separately, and when a generation fails, the funds return to the balance automatically.
The model bill stays predictable, and a failed call costs you nothing.
Trustworthy answers
The biggest risk with models is an answer that sounds plausible yet is false. I build implementations so this can be caught in seconds.
The assistant searches your files and answers from the passages it found, pointing to the exact place in the file.
Every answer shows which document and which passage it came from. Checking it takes seconds.
When the materials hold no answer, the assistant says so plainly and hands the case to a person.
For decisions with financial or legal consequences, the model prepares a proposal and your employee signs off on it.
Cost control
Before the implementation I work out what one call costs and how many there will be per month. You get the bill's range before anything starts.
I set thresholds past which calls are paused and you get a notification. The bill always stays within the agreed limits.
A cheaper, faster model for simple tasks; a stronger one for the hard ones. That choice alone can cut the cost severalfold at the same quality.
Recurring questions are served from cache, without paying for the same thing twice.
Data and security
I use business billing with providers whose API terms exclude submitted content from model training.
Data stays on servers in data centers within the European Union. Where full locality is required, I set up a model running on your own server.
A processing register, a data processing agreement and a description of what data reaches the model and why. Useful during audits — yours and your clients'.
You can see what was asked, what the model answered and what it cost. Without that record you can neither improve quality nor keep the bill in check.
Process
You tell me where someone at the company reads, writes or retypes the same thing over and over. An hour in, it is usually clear what to hand to a model.
I take a sample of your documents and show the result on a real example. You see the quality before you decide anything.
Scope, implementation price and the calculated monthly cost of calls. All in writing, before the start.
The implementation comes with limits, call logging and handling for the cases where the model gets it wrong.
The model works alongside the current routine while we compare results and refine the prompts until the quality settles.
Models change every few months. I check quality, migrate to newer versions and keep the costs in line.
After launch
You see every question, every answer and the cost. It is the simplest way to judge whether the implementation pays off.
A short document on how to use it and what should never be handed to the model. Implementations rarely take root without one.
For the first thirty days I refine the prompts and rules based on what actually happens.
From 300 zł a month: watching quality and costs, and migrating to newer models when better ones appear.
Price
Connecting a ready-made tool to your data starts at 5,000 zł net. An assistant answering from your documents usually starts at 12,000 zł, and a model wired into a whole process at 25,000 zł. The cost of the calls themselves is calculated separately and quoted before launch.
from 5,000 złnet, one-off
What affects the price:
Oversight of a running implementation costs from 300 zł a month and covers watching quality and costs, plus migrating to newer models. Model call fees are billed separately, by actual usage.
Questions
Connecting a ready-made tool to your data starts at 5,000 zł net. An assistant answering from your documents usually starts at 12,000 zł, and a model wired into a whole process at 25,000 zł. On top of that come the model call costs, which I calculate and quote before the start.
It depends on the number of queries. For an assistant handling a few hundred questions a month, the model bill usually stays within a few dozen zloty; with heavy traffic and long documents it runs into the hundreds. I calculate this before the implementation and set limits that pause calls once crossed.
I use business billing with providers whose API terms exclude submitted content from model training. If you require full locality, I set up a model running exclusively on your own server — it costs more, but the data never leaves the company.
I build implementations so the model answers from your documents and cites the source of every answer. When the materials hold no answer, it says so plainly and hands the case to a person. For decisions with financial consequences, the final word always belongs to your employee.
With one task where someone reads or writes the same thing over and over. I run a trial on a sample of your data, show the result, and only then do you decide. This approach costs little and shows right away whether the quality is good enough.
No. The model connects to what you already have: the store, the CRM, the inbox, the document drive or the warehouse software. If a system offers an API or file export, it can be connected.
Most often Claude from Anthropic, because it holds up well on long documents. For images I use image models, for voiceover — speech synthesizers. The choice is described in the quote along with the cost, and when switching to a newer model I verify quality on your own examples.
Yes, and that is usually where it works best. The model reads the content of a request or a document, and the automation passes the result on: to a system, a spreadsheet or the right person. Plain automation without AI has its place too, and I say so whenever it is enough.
Some regional programs cover company digitalization, and AI implementations often qualify. I don't handle the applications themselves, but I will prepare the technical description and the quote in the format the competition paperwork requires.
Related services
Projects rarely stop at one thing — here is the rest of what I do, each with its starting price.
Get a quote
A few sentences are enough: where at your company someone reads or writes the same thing over and over. I'll reply with a proposal, a quote and the calculated monthly cost — usually the same day.